Community-aware diversification of recommendations

Intent-aware methods for recommendation diversification seek to ensure that the recommended items cover so-called aspects, which are assumed to define the user's tastes and interests. Most typically, aspects are item features such as movie or music genres. In recent work, we presented a novel intent-aware diversification method, called Subprofile-Aware Diversification (SPAD). In SPAD, aspects are subprofiles of the active user's profile, detected using an item-item similarity method. In this paper, we propose Community-Aware Diversification (CAD), in which aspects are again subprofiles but are detected indirectly through users who are similar to the active user. We evaluate CAD's precision and diversity on four different datasets, and compare it with SPAD and an intent-aware diversification method called xQuAD. We show that on two of the datasets SPAD outperforms CAD, but for the other two CAD outperforms SPAD. For all datasets, both CAD and SPAD achieve higher precision than xQuAD. When it comes to diversity, xQuAD sometimes results in more diverse recommendations but it is more prone to paying for this diversity with decreases in precision. Arguably, SPAD and CAD strike a better balance between the two.